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[Wearable Smart Cardiac Care: Take Vipasyana for Example]

Hu li za zhi The journal of nursing 2024年第3期71卷 13-19页

作者： Hsi-Lu Chao Sau-Hsuan Wu PhD Associate Professor Department of Computer Science National Yang Ming Chiao Tung University Taiwan ROC. hsiluchao@nycu.edu.tw. PhD Professor Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan ROC.

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical engineering and computer science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

关键词： Apache storm big data real-time streaming framework artificial intelligence heart failure rehabilitation care intelligent cardiac healthcare internet of things virtual reality-based 3D ward round system

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Traffic sign detection and recognition using deep learning-based approach with haze removal for autonomous vehicle navigation

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 7卷

作者： Rani, A. Radha Anusha, Y. Cherishama, S.K. Laxmi, S. Vijaya Associate Professor Department of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC Autonomous) Maisammaguda Hyderabad India

Autonomous vehicle navigation technology is increasing rapidly. However, automatic sign recognition in complex illumination environments like low-light, hazy regions is a significant challenge in in-vehicle navigation. So, haze removal and robust traffic sign detection and recognition (TSDR) are critical for ensuring the vehicle's and its passengers' safety. However, the conventional methods failed to perform both haze removal and TSDR operations simultaneously. Further, the conventional haze removal methods eliminate the wanted pixels, still the presence of haze, which results in reduced traffic sign detection performance. Moreover, the conventional sign recognition methods classify a few types of traffic signs. So, this work aims to develop a unified model for multi-class sign recognition in complex environmental conditions. Therefore, this work introduced the deep learning model for haze removal based on TSDR (DLHR-TSDR). Initially, the CURE-TSD dataset is considered. The haze removal U-network (HRU-Net) module inputs a hazy image and outputs a haze-free image trained to learn the mapping between hazy and haze-free images. Then, the TSDR-convolutional neural network (CNN) module takes the haze-free image from the previous module as input and outputs the location traffic signs in the image. The simulation results on the Carleton University Retinal Eye-Traffic Sign Dataset (CURE-TSD) dataset show that the DLHR-TSDR method developed in the study resulted in 99.01 % accuracy, higher than traditional methods. © 2024 The Authors

关键词： Convolutional neural network Deep learning Haze removal Traffic sign detection and recognition

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Dynamic Defense Model against Eclipse Attacks in Proof-of-Work Blockchain Systems

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Procedia computer science 2024年 235卷 1202-1212页

作者： Surendra Reddy Vinta Sunil A. Patel Aws Zuhair Sameen Mukesh Soni Dr Ihtiram Raza Khan Hayder Mahmood Salman Associate Professor School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Assistant Professor L. D. College of Engineering Ahmedabad College of Medical Techniques Al-Farahidi University Baghdad Iraq Assistant Professor Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab 140413 India Academician Jamia hamdard Delhi Computer Science Department Al-Turath Universiy College Baghdad Iraq

An emerging threat within blockchain systems is the malicious tactic known as eclipse attacks, wherein attackers aim to deceive and gain control over targeted nodes. Despite the increasing popularity of blockchain technology, effective defence mechanisms against eclipse attacks have been lacking. A novel and dynamic defence model designed explicitly for Proof-of-Work (PoW) consensus blockchain systems is proposed to address this critical issue. The model relies on a node-to-node evaluation mechanism to ensure robust protection. By leveraging mutual evaluations conducted between clients and incorporating the renowned Kademlia algorithm, a ranking value is assigned to each node within the defence model. These rankings empower nodes to prudently select trustworthy peers as neighbours based on a threshold comparison, effectively mitigating the risk posed by malicious nodes. Notably, the proposed approach significantly enhances the security of blockchain systems without necessitating modifications to existing protocols or network structures. The effectiveness of the proposed defence model in countering eclipse attacks is extensively validated through a series of experiments. Furthermore, the approach surpasses previous defence strategies, offering a higher probability of successful defence while maintaining minimal overhead. With its straightforward implementation and operational efficiency, the dynamic defence model is an ideal solution for real-world deployment scenarios. This research presents a compelling and robust defence model against eclipse attacks in PoW blockchain systems, addressing the existing gap in defence mechanisms. Furthermore, this advancement paves the way for the wider adoption of blockchain applications across various domains by providing enhanced security, reliability, and trustworthiness.

关键词： Blockchain Eclipse Attacks Dynamic Defence Model Proof-Of-Work (Pow) Consensus Network Structures Targeted Nodes

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Generation of Handwriting Applying RNN with Mixture Density Network

Generation of Handwriting Applying RNN with Mixture Density ...

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International Conference on Automation, Signal Processing, Instrumentation and Control, i-CASIC 2020

作者： Jayashree, D. Pandithurai, O. Shreevathsav, S. Shyamala, P. Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Associate Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India UG students Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (纸本)9789811582202

Handwriting recognition is one of the fields where researchers try to apply different machine learning techniques to identify the words and recreate a meaningful sentences. Recognition has two ways to get an input: online and offline. Online handwriting involves the conversion of text along with continuous input as user writes the text. Offline handwriting involves processing handwriting images without any other data as input. This paper has been worked on using a set of online handwriting data to recognize a variety of features using long short-term memory recurrent neural networks (LSTM RNNs). The trained neural network along with mixture density networks (MDNs) is used to generate offline handwriting samples from text. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

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Enhancing IoT Network Security through AIDriven Intrusion Detection with Hybrid AutoencoderGAN Fusion 23

Enhancing IoT Network Security through AIDriven Intrusion De...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices.

关键词： Deep Learning

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Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India

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Medical Journal Armed Forces India 2021年第3期77卷 302-311页

作者： Maini, Ekta Venkateswarlu, Bondu Maini, Baljeet Marwaha, Dheeraj Research Scholar (Computer Science & Engineering) Dayananda Sagar University Bengaluru India Associate Professor (Computer Science & Engineering) Dayananda Sagar University Bengaluru India Professor Pediatrics Teerthanker Mahaveer Medical College & Research Centre Moradabad India Senior Software Engineer Microsoft India Hyderabad India

Background: In India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India. Methods: A total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet. Results: ML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http://***/predict/ Conclusions: ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected. © 2020

关键词： Affordable healthcare Cardiovascular diseases Early diagnosis Machine learning

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Blockchain effect on E-commerce: A framework for key research areas

Blockchain effect on E-commerce: A framework for key researc...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Nethravathi, K. Panduro-Ramirez, Jeidy Gehlot, Anita Barve, Amit Sanjay, C.P. Ponnusamy, R. Jain University Department of Bms Bangalore India Universidad Tecnológica Del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of Associate Professor & Hod India Department of It& Business Analytics India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350322309

Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based technology are popularly known as "trust building systems"that can be used in multiple business models and standard guidelines which evolved placed above a white period to achieve believe, serviceability, and enforcement in various domains such as cunsumers related business, business related connections have to be questioned and possibly adjusted. By enabling trustless trade interactions that work without specialized intermediaries or even central authority in the case of permission lessblock chains, Blockchain has the power to change how electronic commerce operates. Furthermore, giving universal access to immutable data across the whole supply chain may significantly alter the information and value exchange between businesses and consumers. The methodology presented in this paper are intended to motivate further study of the potential effects of Blockchain on e-commerce. Technology, law, organizational and quality challenges, as well as consumer difficulties, are the four key categories. This paper demonstrates how Blockchain may affect many Electronic commercial components in these relevant fields. © 2023 IEEE.

关键词： Data mining

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Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing

Improving Robotic Arms Through Natural Language Processing, ...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Pascal Sikorski Kaleb Yu Lucy Billadeau Flavio Esposito Hadi AliAkbarpour Madi Babaias Undergraduate Research Assistant Computer Science Department Saint Louis University Saint Louis USA Undergraduate Research Assistant Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA Associate Professor Computer Science Department Saint Louis University Saint Louis USA Assistant Professor Computer Science Department Saint Louis University Saint Louis USA CBL Assistant Professor of Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://***/EnhancedArmEdgeNLP.

关键词： computer vision Mechatronics Source coding Machine vision Large language models Prototypes Focusing Manipulators Natural language processing Edge computing

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Design and Development of a Novel Algorithm to Predict Fake Logo using Learning based Digital Image Analysis Methodology

Design and Development of a Novel Algorithm to Predict Fake ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Nagajothi Shabana Memon G. Mohan Parashuram Shankar Vadar Anita Soni S. Prince Sahaya AP/ Mathematics Nandha Engineering College (Autonomous) Erode- 52 Assistant Professor Bharati Vidyapeeth (Deemed to be University) Institute of Management Kolhapur Associate Professor Department of Mathematics K.S.Rangasamy College of Technology Tiruchengode TamilNadu Assistant Professor Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Department of Computer Science and Engineering IES University Bhopal Assistant Professor (Sl.G) Department of Computer Science Engineering Sri Ramkrishna Engineering College Coimbatore

In the realm of fake logo detection, the continuous surge in digital content manipulation necessitates advanced and efficient methodologies for safeguarding brand integrity. This study explores the application of diverse machine learning models, ranging from traditional algorithms like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to sophisticated deep learning (DL) architectures such as VGG19, Inception, and Convolutional Neural Networks (CNN). The proposed methodology combines feature extraction using VGG19 with classification using an Inception model, presenting a novel approach to enhance detection accuracy. Evaluation metrics, including accuracy, precision, specificity, and sensitivity, which enhance the performance of each model. Computational efficiency is also analyzed, providing a comprehensive understanding of the trade-offs between accuracy and processing speed. The proposed methodology emerges as a promising solution, achieving a remarkable accuracy of 97% while maintaining efficiency. This study contributes to the evolving landscape of fake logo detection, offering valuable insights for practical implementation in scenarios demanding precision, speed, and reliability.

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